Understanding the biological networks that regulate oncogenic events and influence the inherent radiosensitivity of tumors is central to the development of personalized treatment strategies in radiation oncology, including targeted and improved therapeutic interventions. During the last two decades, many key components and signaling pathways in the oncogenic network have been elucidated by studying radiophenotypic changes after network components are perturbed. However, the dynamics of network component interactions have remained mostly undefined, largely due to lack of accurate testing methods.
The generation of high-throughput datasets in the “omic” era has been central to the development of a systems-view of complex biological systems. In systems biology, the goal is to understand the dynamics of the system and how components interact during operation (H. Kitano, Computational Systems Biology, Nature 420:6912, 2002, 206-210) (H. Kitano, Systems Biology: A Brief Overview, Science 295:5560, 2002, 1662-1664) (L. Hood, J. Heath, Systems Biology and New Technologies Enable Predictive and Preventative Medicine, Science, 306:5696, 2004, 640-643) (L. Hood, R. Perlmutter, The Impact of Systems Approaches on Biological Problems in Drug Discovery, Nat. Biotech., 22:10, 2004, 1215-1217). To study complex biological interactions within a network model, novel methods are needed. A central experimental approach in molecular biology has focused on studying biological systems after components are perturbed by activation/inactivation. A problem of this approach is that it is unable to capture and study the continuous nature of many phenotypic features in diseased and normal states. An alternative approach is a systems-view of biological networks where the focus is on understanding the dynamics and structure of the system of interest. A common feature of systems biology is the development of dry computational models which exploit comprehensive datasets of high-throughput measurements. A common denominator in these models is that biological hypothesis can be generated for testing in “wet” experiments, thus allowing the validation of the models and the dynamics studied. Computational models have been key in the development of central concepts in neurobiology.